This invention relates generally to computer-implemented methods and apparatus for modeling energy systems.
Profitable operation of an energy system is of particular interest in industry. Optimization of operation requires predictive models depicting thermo-economic performance of the energy system, and a method to solve those models in order to determine the most profitable way to operate the system. The performance of many of these energy systems is strongly influenced by various factors including environmental factors, operational factors or constraints (e.g. production limits and system output requirements).
Energy systems vary in configuration of both equipment setup and processes. One known plant optimization software configuration described in U.S. Pat. No. 6,591,225, issued Jul. 8, 2003, has set a standard for efficient optimization of classical configurations of power plants. However, the structure of this software does not permit it to be extended simply beyond the realm of a particular set of power plant equipment configurations. In particular, software limitations limit the usefulness of this software configuration for power plants of complex configuration, especially those connected to or serving process plants. Two examples of such process plants are chemical process plants and water desalination plants. Furthermore, at least one other known configuration of power plant optimization software uses an essentially non-linear formulation of the power generation process and generally bases its optimization on non-linear optimization algorithms.
The fact that the equations representing energetic performances of most energy systems are inherently non-linear complicates the process of optimization. At the same time, a common expectation of any optimization system is to select which equipment to operate and which to shutdown during times of low load demand. This would require that the optimization system include selection switches such as integer variables. Optimization algorithms dealing with both non-linear equations and integer variables normally require computational times exceeding practically acceptable or useful limits.